The invention relates generally to image processing, and more particularly, to methods and systems which use a gradient-based image analysis in conjunction with orientation-isotropy adaptive filtering to enhance and eliminate noise content during image feature restoration.
Almost every kind of data contains noise. Noise reduction is a required step for any sophisticated algorithm used in image processing. In medical image processing applications such as X-ray, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT) and others, noise manifest during image capture, enhancement, and restoration needs to be suppressed while the original image structure is preserved. In medical imaging, image enhancement is important because it allows physicians to obtain a better visual interpretation, especially when viewing small structures (i.e. thin vessels). Moreover, enhancement is a preprocessing step for subsequent medical analysis, such as anatomy segmentation or registration of images from different modalities.
Most medical images have image quality characteristics such as contrast, sensitivity, detail (blurring), visual noise, spatial characteristics, and artifacts. Noise present in digital imaging is primarily due to the quantum noise inherent in photon detection and electronic noise. Electronic noise is relatively constant. However, quantum noise is related to the number of photons counted. Noise in an image is an undesirable, relatively high detail characteristic. Therefore, when images are processed to increase, or enhance detail, the processing also increases the visibility of the noise. When reconstructing images from data, algorithms used employ filters that impact noise in the final image. Digital image processing generally reduces noise by blurring the image creating an undesirable tradeoff.
Using CT as one example, there are several adjustable protocol factors that have an effect on image noise. Reducing voxel size, which increases detail, also increases noise because fewer protons are absorbed or captured in each voxel. Noise can be decreased by increasing the tube current—time product (mAs). However, this increases the radiation dose to the patient.
Noise is produced by the random variation, or difference, in the number of photons from one voxel to another. The statistical variation in image noise increases as the number of photons, exposure, and dose, is decreased. Small voxels, as used for better detail, capture less photons and result in more noise.
For example, if a CT slice thickness is decreased to improve image detail, the noise level will increase because of the smaller voxels. If the mAs is then increased to maintain the same noise level, the radiation dose will be increased. This is why thin CT slices are only used when necessary from a clinical perspective.
Filter algorithms can either decrease or increase noise content depending on what type is selected. Filtering is a preliminary process in many medical image processing applications. It is a fundamental operation in low-level computer vision, aiming at restoring a noise-corrupted image to its noiseless counterpart. Any post-processing tasks such as segmentation and feature enhancement benefit from noise reduction.
Diffusion equations with scalar and tensor valued diffusivities have been applied to MRIs and 3D rotational angiography (RA) for edge preserving smoothing. Magnetic resonance angiography (MRA) denoising with adaptive filtering in the Fourier domain has been proposed. Non-linear noise reduction techniques in CT imaging have also been investigated.
One adaptive filtering technique reduces the effects of partial volume averaging by resampling the data to a lattice with higher sample density thereby reducing image noise level. Resampling is achieved by constructing filter sets that have subpixel offsets relative to the original sampling lattice. The filters are also frequency corrected for anisotropic voxel dimensions. The shift and the voxel dimensions are described by an affine transform and provide a model for tuning the filter frequency functions.
Signal processing based methods, such as using a wavelet transform can be used in image denoising since the noise is evenly distributed among the wavelet coefficients and typically is small in magnitude. With a properly chosen threshold, noise can be suppressed.
The discrete wavelet transform is very efficient from the computational point of view. However, one drawback is that it is not translation invariant. Translations of the original signal lead to different wavelet coefficients.
The wavelet transform gives detailed spatial-frequency information and provides a possibility for better discrimination between noise and data. However, successful exploitation of the wavelet transform has not been achieved.
The above methods do not provide an effective balance between noise reduction and structure preservation due to the complexity of the noise statistics. At the same time, a phenomenon known as overshooting may happen near step-edge regions of an image.
Partial differential equation based methods have been attempted for use in image processing for their ability to reduce noise while preserving important features of the images. The use of linear isotropic diffusion equations equivalent to Gaussian filtering may result in edge blurring or structure relocation. Nonlinear anisotropic diffusion equations proposed by Perona and Malik rely upon the diffusion of image gray values and depend on gradient magnitude where the diffusion is stopped across edges or discontinuities. Coherence nonlinear anisotropic diffusion proposed by Weickert is more directional in both the gradient and the contour directions but may produce a brushstroke effect in the non-structure regions due to errors in local structure estimation.
While there exist various techniques for image noise reduction, an ideal filtering technique has yet to be introduced. Achieving image noise reduction has proven problematic most often due to new problems arising while undergoing operations to reduce noise content. What is desired is a method and system for removing existing noise content and obviating the introduction of new noise content during image feature enhancement.